Hi there, I want to talk about artificial intelligence (AI) in particular, despite all the hoopla, I don’t see a lot of companies using it.
Could you kindly tell us (roughly) how AI was employed if you have worked on an AI project or product?
Thanks in advance.
I’m employed at a service desk. Our products, chatbots, are entirely AI and ML based.
However, more than genuine AI features, I see AI impacting businesses within.
GPT4 helps engineers write code more efficiently and quickly, which simplifies the process of developing software.
I’m curious how this may affect future product development.
In reality, companies are increasingly leveraging artificial intelligence to gain a competitive edge and improve various aspects of their operations. AI is being used in diverse industries, such as healthcare, finance, retail, and manufacturing, to automate processes, enhance customer experiences, optimize supply chains, and make data-driven decisions.
However, implementing AI solutions requires careful planning and understanding of the specific business needs and challenges. It is important for companies to invest in the necessary expertise and resources to effectively harness the power of AI for their unique business goals. This includes hiring AI experts or partnering with AI-driven companies to develop and customize AI algorithms and applications that align with their objectives. Moreover, companies should ensure that they have a robust infrastructure in place to collect, store, and analyze the vast amount of data required for AI operations.
Additionally, businesses must consider the ethical implications of AI, including ensuring fairness, transparency, and accountability in its decision-making processes, and protecting customer privacy and data security. By addressing these considerations, companies can successfully integrate AI into their operations and gain a competitive edge in today’s fast-paced and data-driven business landscape.
AI has been employed in various industries and sectors, such as healthcare, finance, and retail. In healthcare, AI has been used to analyze medical data and assist in diagnosing diseases, leading to more accurate and timely treatments. In finance, AI algorithms have been utilized for fraud detection and risk assessment, improving the overall security of financial transactions.
Additionally, in the retail industry, AI-powered recommendation systems have enhanced customer experiences by personalizing product suggestions based on individual preferences. These examples highlight the diverse applications of AI across various industries, demonstrating its ability to revolutionize processes and drive innovation. Moreover, in the field of transportation, AI has been employed to optimize traffic flow and reduce congestion, ultimately improving commuting times and enhancing overall efficiency.
Furthermore, AI has also made significant strides in the field of agriculture, where it has been used to monitor crop health, predict weather patterns, and optimize irrigation schedules, enabling farmers to make informed decisions and maximize their yields. These diverse applications of AI underscore its potential to transform and improve countless sectors of the economy.
Within the EdTech industry, artificial intelligence (AI) is utilized to grade students’ written answers to prompts. Additionally, this same technology provides students with real-time feedback on their written responses, allowing them to refine their responses further. This innovative use of AI in grading and providing feedback not only saves teachers’ time but also ensures consistent and objective evaluation.
Moreover, the integration of AI technology in education fosters personalized learning experiences by tailoring feedback to individual students’ needs and helping them improve their writing skills effectively. As AI technology continues to advance, it has the potential to revolutionize education by providing even more personalized and adaptive learning experiences.
For example, AI chatbots can simulate conversations and provide immediate language practice for students, enhancing their speaking and listening skills. Additionally, virtual reality (VR) technology can create immersive learning environments, allowing students to explore subjects like history and science in a more engaging and interactive way. The integration of AI in education holds great promise for empowering students and teachers alike to reach their full potential.
Some of the examples of applied AI just off the top of my head.
In theory, this makes sense to me, but could you please identify one or two of these and describe what “AI” actually means in each case, where it’s being used, and how the PM fits in?
I’m really interested because I worked as an APM for a product that had many of the features you listed, but we didn’t use any proprietary tools—instead, we relied on third-party tools that used machine learning and algorithms, like natural language processing (NLP)—to decide which products appear where in search results or how product reviews are moderated.
Are we using AI to describe every output that isn’t directly our own? I apologize for my confusion.
The term artificial intelligence (AI) is quite nebulous and is frequently used synonymously with “machine learning” or even algorithms.
NLP is “AI” in that it draws conclusions from new data using machine learning models that have been trained on a set of data. That is all there is to it.
Like any other PM, an AI/ML PM has objectives and key performance indicators. Simply put, AIML is a more effective way to solve such KPIs than other manual or conventional methods.
Consider a recommender system, for instance. You want to be able to display products to potential clients on your e-commerce website that pique their interest so they will make more purchases (your KPI). To accomplish this, you attempt to determine which goods are bought in tandem by looking through the purchase histories of all of your prior clients. There are constraints to doing a standard statistical study and then running a lookup against this every time a customer shops. What happens if your product catalog isn’t static? Your supply team recently added a brand-new product for which your statistical model is unable to provide an answer.
As an alternative, you create a machine learning model and train it using your purchasing history. In addition to categorizing products, it also assigns a ranking to new products based on its learnings, enabling it to suggest the best product to a particular customer even in cases when you have never sold that product or met that customer before. That’s the part about learning.
It is once more your KPI as the PM to increase product sales. To increase this model’s performance and enable it to generate ever-better recommendations, you will need to fine-tune, edit, and iterate on it. This is accomplished by estimating the model’s potential performance using newly available data in addition to examining the model’s current performance. Perhaps more client data will result in more accurate recommendations. Perhaps retrain your model more often. etc.
I’m interested in point 4. Would you have an example and perhaps links to any resources I could read?
Thank you
In its most basic form, machine learning eliminates the uncertainty surrounding which clients to reach out to, when to do so, how frequently to do so, and what kind of material to send them.
Sadly, I lack resources because the majority of businesses that engage in this jealously guard their specific methods. Fundamentally, though, it’s the same as any other ML flavor.
I have seen the following applications in my professional exposure and experiences thus far:
Prompt engineering is used to handle specific use cases for customer care, support, and brand relationship management; it also eliminates the need for human involvement in funnel activities (such as sales support, Q&A, and so on) and facilitates low-complexity, <30-second interactions.
Generation and administration of content in almost any setting you can think of.
Applications in specialization and specialized markets that combine the first two.
I have many, many more uses in mind that may be useful. These are currently the most popular ones in the area.
I currently work on an AI team and have previously worked on AI products. I would argue that this was a more “rudimentary” application of AI in eCommerce, where we utilized it extensively for automatic catalog protections, brand identification at scale, and other things. The key benefit of AI in this context is that it can reason in a feature space that is too complex for a person to understand, assuming you have enough data.
My current work involves a combination of applied research and engineering, with a focus on enhancing the fairness of ranking and recommender systems. Despite receiving less attention than generative AI, these systems are becoming more and more crucial because an R&R system selects almost all of the content that users see or interact with.
It was utilized to select the content that appeared in feeds on social media. After you’ve given several instances of the requests, it’s used to classify customer requests. Additionally, ranking and recommendation systems play a vital role in various domains such as e-commerce, online advertising, and content platforms. They help personalize user experiences by suggesting relevant products, advertisements, and articles based on their preferences and behaviors. These systems rely on applied research and engineering techniques to ensure fair and accurate content selection for users across different platforms.
Right now, everyone is attempting to use it for chat-based interfaces, but it can be used to improve fundamental help jobs in practically any software field. I say boost because AI/ML goes wrong and you have to design your product to empower people. By incorporating AI and ML into fundamental help jobs, software fields can enhance the efficiency and effectiveness of their support systems. This technology can provide personalized assistance, identify common issues, and offer solutions, ultimately empowering users to troubleshoot problems on their own. However, it is crucial to remember that AI and ML should be used as tools to assist humans rather than replace them entirely, ensuring a balance between automation and human intervention.
My company manages social media marketing through YouTube videos, tweets, and other content descriptions.
Previously, a word selector was used to search for names that matched our database, followed by a manual approval phase. We can now analyze significantly more data in a much faster and more economical manner by using OpenAI, feeding in the title and description, and using it to output JSON or whatever format is required of the companies mentioned, what kind of campaign it was (i.e., affiliates, sponsorships), and so on.
As the PO at a startup, I’ve been actively involved with the data and prompts (prompt engineering, hehe) to make sure the outcomes match our goals. It’s been an incredibly fascinating process.
Additionally, I’ve been leveraging AI to create my own tools that make product ownership easier. One such tool is sprintsparrow.com, which is a website that requires some work but allows you to input vague comments for enhancement or bug reports, and the website will create a fully developed ticket.
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